Collaborative platforms are becoming a necessity for enterprises and organizations since they have become extended and have to work with other organizations in joint projects. Knowledge management is considered one of the critical successes of collaborative tools and platforms, especially collaborative ontologies. This paper aims to propose a new approach for developing a collaborative ontology that can be used to support a collaborative platform. Our approach begins with the idea of using a business process model of the collaborative situation, represented in BPMN notation, and to transform it into a collaborative ontology. 

Our collaborative process will be modeled collaboratively, and its transformation to an ontology will be done through a first transformation to Subject oriented BPM (S-BPM). We also proposed to validate our collaborative ontology using competency questions that will be formulated at the beginning by domain experts and the ontologist and verified at the end by the ontologist. Fiscal code generation of a newborn was adopted as a case study to prove the importance of this approach.

Compared to other approaches cited in the literature, our approach allows the construction of a collaborative ontology, that does not need enrichment perspectives, and that involves all collaborative partners during its construction process.

Unmanned aerial vehicles (UAVs) have gained a lot of attention from researchers due to their hovering and vertical take-off and landing. Different techniques and methods are being employed to imple-ment UAVs. The QNET 2.0 VTOL board, specially de-signed for NI ELVIS II, is an important platform in the field of unmanned aerial vehicles (UAV). It is a helpful tool to demonstrate the essentials of vertical take-off and landing flight control (VTOL) at educational institutes. The PID controller installed in QNET 2.0 VTOL board is manually tuned is usually done by a skilled operator. This process of tuning is time-consuming and requires an expert’s knowledge. Although PID control of various sys-tems has been reported in the literature, its use is limited in nonlinear systems. For nonlinear systems. Fuzzy logic is suitable due to its nonlinearity capability. The purpose of this research is to study the dynamics of the QNET 2.0 VTOL model, simulate the flight control model in Lab-VIEW and to design an auto-tuned PID controller using Fuzzy logic for QNET 2.0 VTOL model in LabVIEW environment. This study shows that Fuzzy based auto-tuned PID controller controls the pitch angle of the QNET 2.0 VTOL model and gives promising results as compared to the existing PID controller in terms of auto-tuning in real-time and stability of the system.

The multi-path fading environment is a major challenge of UWB devices. So, a MIMO system is one of the importance techniques which exploited to mitigate such problems. In this research, a MIMO system with eight ports consists of four antenna elements has proposed. Y-shaped of patch microstrip has chosen to design each element to enhance the bandwidth of the proposed system. In order to achieve a good isolation, the geometry of the ground layer of the proposed antenna element has based on quasi-self-complementary structure. The proposed model has a compact size because it facilitates with dual polarized ports which increase the capacity and maintaining an acceptable size. The results are show that a bandwidth of 2.06 GHz has obtained with operating frequency of 8.73 GHz for single elements and the integrated MIMO system when excited simultaneously.

Gross Domestic Product (GDP) per capita is a critical degree of a nation's monetary growth that records for its number of people. A balanced participation ratio of both males and females in the industry by ensuring skilled and technical education for all provides a stable economic development in a country. Population and Gender impact on GDP prices in Bangladesh were investigated in this study. To address the effect of gender factors in GDP prices, we considered the following parameters: year, combined population, male population, and female population. Based on these parameters, the global domestic product-current prices of Bangladesh were analyzed. For the predictive analysis, we have used various machine learning algorithms to make prediction and visualization of the predicted output. A quantitative analysis was also performed to examine the correlation among different gender factors with the growth of GDP. Based on analysis and study results, we can say that the machine learning approach could be applied efficiently in numerous applications of GDP forecasting.

Fake news dissemination is a critical issue in today’s fast-changing network environment. Existing classification models for fake news detection have not completely stopped the spread because of their inability to accurately classify news, thus leading to a high false alarm rate. This study proposed a model that can accurately identify and classify deceptive news articles content infused on social media by malicious users. The news content, social-context features and the respective classification of reported news was extracted from the PHEME dataset using entropy-based feature selection. The selected features were normalized using Min-Max Normalization techniques. A predictive fake news detection model was formulated as a stacked ensemble of three algorithms. The model was simulated and its performance was evaluated by benchmarking with an existing model using detection accuracy, sensitivity, and precision as metrics. The result of the evaluation showed a higher 17.25% detection accuracy, 15.78% sensitivity, but lesser 0.2% precision than the existing model. Thus, the proposed model detects more fake news instances accurately based on news content and social content perspectives. This indicates that the proposed classification model has a better detection rate, reduces the false alarm rate of news instances and thus detects fake news more accurately.